Graduate University of Advanced Technology

About: Graduate University of Advanced Technology is a education organization based out in Kerman, Iran. It is known for research contribution in the topics: Carbon paste electrode & Electrochemical gas sensor. The organization has 890 authors who have published 2169 publications receiving 31027 citations.

A Memory-Based Genetic Algorithm for Optimization of Power Generation in a Microgrid

Abstract: In smart grids, one of the most important objectives is the ability to improve the grid's situational awareness and allow for fast-acting changes in power generation. In such systems, an energy management system should gather all the needed information, solve an optimization problem, and communicate back to each distributed energy resource (DER) its correct allocation of energy. This paper proposes a memory-based genetic algorithm (MGA) that optimally shares the power generation task among a number of DERs. The MGA is utilized for minimization of the energy production cost in the smart grid framework. It shares optimally the power generation in a microgrid including wind plants, photovoltaic plants, and a combined heat and power system. In order to evaluate the performance of the proposed approach, the results obtained by the MGA are compared with the results found by a genetic algorithm and two variants of particle swarm optimization. Simulation results accentuate the superiority of the proposed MGA technique.

A new optimization method based on COOT bird natural life model

Abstract: Recently, many intelligent algorithms have been proposed to find the best solution for complex engineering problems. These algorithms can search volatile and multi-dimensional solution spaces and find optimal answers timely. In this paper, a new meta-heuristic method is proposed that inspires the behavior of the swarm of birds called Coot. The Coot algorithm imitates two different modes of movement of birds on the water surface: in the first phase, the movement of birds is irregular, and in the second phase, the movements are regular. The swarm moves towards a group of leading leaders to reach a food supply; the movement of the end of the swarm is in the form of a chain of coots, each of coot which moves behind its front coots. The algorithm then runs on a number of test functions, and the results are compared with well-known optimization algorithms. In addition, to solve several real problems, such as Tension/Compression spring, Pressure vessel design, Welded Beam Design, Multi-plate disc clutch brake, Step-cone pulley problem, Cantilever beam design, reducer design problem, and Rolling element bearing problem this algorithm is used to confirm the applicability of this algorithm. The results show that this algorithm is capable to outperform most of the other optimization methods. The source code is currently available for public from: https://www.mathworks.com/matlabcentral/fileexchange/89102-coot-optimization-algorithm .